Introduction to Operations ResearchCD-ROM contains: Student version of MPL Modeling System and its solver CPLEX -- MPL tutorial -- Examples from the text modeled in MPL -- Examples from the text modeled in LINGO/LINDO -- Tutorial software -- Excel add-ins: TreePlan, SensIt, RiskSim, and Premium Solver -- Excel spreadsheet formulations and templates. |
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Page 14
... objective functions f ( x ) = ( f1 ( x ) , f2 ( x ) , ... , fm ( x ) ) considered in the above formulation . Each objective function can be either minimized or maximized . The duality principle ( Deb , 1995 ; Rao , 1984 ; Reklaitis et ...
... objective functions f ( x ) = ( f1 ( x ) , f2 ( x ) , ... , fm ( x ) ) considered in the above formulation . Each objective function can be either minimized or maximized . The duality principle ( Deb , 1995 ; Rao , 1984 ; Reklaitis et ...
Page 43
James C. Bezdek. Objective Function Clustering 3 S8 illustrates some of the difficulties inherent with cluster analysis ; its aim is to alert investigators to the fact that various algorithms can suggest radically different substructures ...
James C. Bezdek. Objective Function Clustering 3 S8 illustrates some of the difficulties inherent with cluster analysis ; its aim is to alert investigators to the fact that various algorithms can suggest radically different substructures ...
Page 135
... objective function dispersion fits the upper limit, but also those whose objective function dispersion becomes smaller (i.e., more robust) than the upper limit. It means that this approach can search for a wider range of robust optimal ...
... objective function dispersion fits the upper limit, but also those whose objective function dispersion becomes smaller (i.e., more robust) than the upper limit. It means that this approach can search for a wider range of robust optimal ...
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Introduction to Operations Research Frederick S. Hillier,Gerald J. Lieberman No preview available - 2001 |
Common terms and phrases
activity algebraic algorithm allowable range artificial variables b₂ basic solution c₁ c₂ changes coefficients column Consider the following cost Courseware CPLEX decision variables dual problem dual simplex method dynamic programming entering basic variable estimates example feasible region feasible solutions final simplex tableau final tableau following problem formulation functional constraints Gaussian elimination given goal programming graphical identify increase initial BF solution integer iteration leaving basic variable linear programming model linear programming problem LINGO LP relaxation lution Maximize Z maximum flow problem Minimize needed node nonbasic variables nonnegativity constraints objective function obtained optimal solution optimality test parameters path plant presented in Sec primal problem Prob procedure range to stay resource right-hand sides sensitivity analysis shadow prices shown slack variables solve the model Solver spreadsheet step subproblem surplus variables Table tion unit profit values weeks Wyndor Glass x₁ zero